COMP9318: Data Warehousing and Data Miningcs9318/19t1/lect/1intro.pdf4 Evolution of Database Technology n 1960s: n Data collection, database creation, IMS and network DBMS n 1970s:

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COMP9318: Data Warehousing and Data Mining

— L1: Introduction —

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Chapter 1. Introduction

n Motivation: Why data mining?

n What is data mining?

n Data Mining: On what kind of data?

n Data mining functionality

n Are all the patterns interesting?

n Classification of data mining systems

n Major issues in data mining

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Necessity Is the Mother of Inventionn Data explosion problem

n Automated data collection tools and mature database technology lead to tremendous amounts of data accumulated and/or to be analyzed in databases, data warehouses, and other information repositories

n We are drowning in data, but starving for knowledge!

n Solution: Data warehousing and data mining

n Data warehousing and on-line analytical processing

n Mining interesting knowledge (rules, regularities, patterns, constraints) from data in large databases

Who could be expected to digest millions of records, each having tens or hundreds of fields?

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Evolution of Database Technologyn 1960s:

n Data collection, database creation, IMS and network DBMSn 1970s:

n Relational data model, relational DBMS implementationn 1980s:

n RDBMS, advanced data models (extended-relational, OO, deductive, etc.) n Application-oriented DBMS (spatial, scientific, engineering, etc.)

n 1990s: n Data mining, data warehousing, multimedia databases, and Web

databasesn 2000s

n Stream data management and miningn Data mining with a variety of applicationsn Web technology and global information systems

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What Is Data Mining?

n Data mining (knowledge discovery from data)

n Extraction of interesting (non-trivial, implicit, previously

unknown and potentially useful) patterns or knowledge from

huge amount of data

n Data mining: a misnomer?

n Alternative names

n Knowledge discovery (mining) in databases (KDD), knowledge

extraction, data/pattern analysis, data archeology, data

dredging, information harvesting, business intelligence, etc.

n Watch out: Is everything “data mining”?

n (Deductive) query processing.

n Expert systems or small ML/statistical programs

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Why Data Mining?—Potential Applications

n Data analysis and decision supportn Market analysis and management

n Target marketing, customer relationship management (CRM), market basket analysis, cross selling, market segmentation

n Risk analysis and management

n Forecasting, customer retention, improved underwriting, quality control, competitive analysis

n Fraud detection and detection of unusual patterns (outliers)

n Other Applicationsn Text mining (news group, email, documents) and Web miningn Stream data miningn DNA and bio-data analysis

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Market Analysis and Management

n Where does the data come from?

n Credit card transactions, loyalty cards, discount coupons, customer complaint calls, plus (public) lifestyle studies

n Target marketing

n Find clusters of “model” customers who share the same characteristics: interest, income level, spending habits, etc.

n Determine customer purchasing patterns over time

n Cross-market analysis

n Associations/co-relations between product sales, & prediction based on such association

n Customer profiling

n What types of customers buy what products (clustering or classification)

n Customer requirement analysis

n identifying the best products for different customers

n predict what factors will attract new customers

n Provision of summary information

n multidimensional summary reports

n statistical summary information (data central tendency and variation)

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Corporate Analysis & Risk Management

n Finance planning and asset evaluationn cash flow analysis and predictionn contingent claim analysis to evaluate assets n cross-sectional and time series analysis (financial-ratio, trend

analysis, etc.)n Resource planning

n summarize and compare the resources and spendingn Competition

n monitor competitors and market directions n group customers into classes and a class-based pricing proceduren set pricing strategy in a highly competitive market

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Fraud Detection & Mining Unusual Patterns

n Approaches: Clustering & model construction for frauds, outlier analysisn Applications: Health care, retail, credit card service, telecomm.

n Auto insurance: ring of collisions n Money laundering: suspicious monetary transactions n Medical insurance

n Professional patients, ring of doctors, and ring of referencesn Unnecessary or correlated screening tests

n Telecommunications: phone-call fraudn Phone call model: destination of the call, duration, time of day or

week. Analyze patterns that deviate from an expected normn Retail industry

n Analysts estimate that 38% of retail shrink is due to dishonest employees

n Anti-terrorism

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Other Applications

n Sports

n IBM Advanced Scout analyzed NBA game statistics (shots blocked,

assists, and fouls) to gain competitive advantage for New York

Knicks and Miami Heat

n Astronomy

n JPL and the Palomar Observatory discovered 22 quasars with the

help of data mining

n Internet Web Surf-Aid

n IBM Surf-Aid applies data mining algorithms to Web access logs

for market-related pages to discover customer preference and

behavior pages, analyzing effectiveness of Web marketing,

improving Web site organization, etc.

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Data Mining: A KDD Process

n Data mining—core of knowledge discovery process

Data Cleaning

Data Integration

Databases

Data Warehouse

Task-relevant Data

Selection

Data Mining

Pattern Evaluation

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Steps of a KDD Process

n Learning the application domainn relevant prior knowledge and goals of application

n Creating a target data set: data selectionn Data cleaning and preprocessing: (may take 60% of effort!)n Data reduction and transformation

n Find useful features, dimensionality/variable reduction, invariant representation.

n Choosing functions of data mining n summarization, classification, regression, association, clustering.

n Choosing the mining algorithm(s)n Data mining: search for patterns of interestn Pattern evaluation and knowledge presentation

n visualization, transformation, removing redundant patterns, etc.n Use of discovered knowledge

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Data Mining and Business IntelligenceIncreasing potentialto supportbusiness decisions End User

BusinessAnalyst

DataAnalyst

DBA

MakingDecisions

Data PresentationVisualization Techniques

Data MiningInformation Discovery

Data Exploration

OLAP, MDA

Statistical Analysis, Querying and Reporting

Data Warehouses / Data Marts

Data SourcesPaper, Files, Information Providers, Database Systems, OLTP

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Architecture: Typical Data Mining System

Data Warehouse

Data cleaning & data integration Filtering

Databases

Database or data warehouse server

Data mining engine

Pattern evaluation

Graphical user interface

Knowledge-base

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Data Mining: On What Kinds of Data?

n Relational databasen Data warehousen Transactional databasen Advanced database and information repository

n Object-relational databasen Spatial and temporal datan Time-series data n Stream datan Multimedia databasen Heterogeneous and legacy databasen Text databases & WWW

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Data Mining Functionalities

n Concept description: Characterization and discriminationn Generalize, summarize, and contrast data characteristics, e.g., dry

vs. wet regions

n Association (correlation and causality)n Diaper à Beer [0.5%, 75%]

n Classification and Predictionn Construct models (functions) that describe and distinguish classes

or concepts for future predictionn E.g., classify countries based on climate, or classify cars based

on gas mileagen Presentation: decision-tree, classification rule, neural networkn Predict some unknown or missing numerical values

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Data Mining Functionalities (2)

n Cluster analysisn Class label is unknown: Group data to form new classes, e.g.,

cluster houses to find distribution patternsn Maximizing intra-class similarity & minimizing interclass similarity

n Outlier analysisn Outlier: a data object that does not comply with the general

behavior of the datan Noise or exception? No! useful in fraud detection, rare events

analysisn Trend and evolution analysis

n Trend and deviation: regression analysisn Sequential pattern mining, periodicity analysisn Similarity-based analysis

n Other pattern-directed or statistical analyses

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Are All the “Discovered” Patterns Interesting?

n Data mining may generate thousands of patterns: Not all of them are interestingn Suggested approach: Human-centered, query-based, focused mining

n Interestingness measuresn A pattern is interesting if it is easily understood by humans, valid on new

or test data with some degree of certainty, potentially useful, novel, or validates some hypothesis that a user seeks to confirm

n Objective vs. subjective interestingness measuresn Objective: based on statistics and structures of patterns, e.g., support,

confidence, etc.n Subjective: based on user’s belief in the data, e.g., unexpectedness,

novelty, actionability, etc.

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Can We Find All and Only Interesting Patterns?

n Find all the interesting patterns: Completeness

n Can a data mining system find all the interesting patterns?

n Heuristic vs. exhaustive search

n Association vs. classification vs. clustering

n Search for only interesting patterns: An optimization problem

n Can a data mining system find only the interesting patterns?

n Approaches

n First generate all the patterns and then filter out the uninteresting ones.

n Generate only the interesting patterns—mining query optimization

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Data Mining: Confluence of Multiple Disciplines

Data Mining

Database Systems Statistics

OtherDisciplines

Algorithm

MachineLearning Visualization

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Data Mining: Classification Schemes

n General functionalityn Descriptive data mining n Predictive data mining

n Different views, different classificationsn Kinds of data to be minedn Kinds of knowledge to be discoveredn Kinds of techniques utilizedn Kinds of applications adapted

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Multi-Dimensional View of Data Miningn Data to be mined

n Relational, data warehouse, transactional, stream, object-oriented/relational, active, spatial, time-series, text, multi-media, heterogeneous, legacy, WWW

n Knowledge to be minedn Characterization, discrimination, association, classification,

clustering, trend/deviation, outlier analysis, etc.n Multiple/integrated functions and mining at multiple levels

n Techniques utilizedn Database-oriented, data warehouse (OLAP), machine learning,

statistics, visualization, etc.n Applications adapted

n Retail, telecommunication, banking, fraud analysis, bio-data mining, stock market analysis, Web mining, etc.

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Major Issues in Data Miningn Mining methodology

n Mining different kinds of knowledge from diverse data types, e.g., bio, stream, Web

n Performance: efficiency, effectiveness, and scalabilityn Pattern evaluation: the interestingness problemn Incorporation of background knowledgen Handling noise and incomplete datan Parallel, distributed and incremental mining methodsn Integration of the discovered knowledge with existing one: knowledge fusion

n User interactionn Data mining query languages and ad-hoc miningn Expression and visualization of data mining resultsn Interactive mining of knowledge at multiple levels of abstraction

n Applications and social impactsn Domain-specific data mining & invisible data miningn Protection of data security, integrity, and privacy

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Summary

n Data mining: discovering interesting patterns from large amounts of data

n A natural evolution of database technology, in great demand, with wide applications

n A KDD process includes data cleaning, data integration, data selection, transformation, data mining, pattern evaluation, and knowledge presentation

n Mining can be performed in a variety of information repositoriesn Data mining functionalities: characterization, discrimination,

association, classification, clustering, outlier and trend analysis, etc.n Data mining systems and architecturesn Major issues in data mining

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A Brief History of Data Mining Society

n 1989 IJCAI Workshop on Knowledge Discovery in Databases (Piatetsky-

Shapiro)

n Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991)

n 1991-1994 Workshops on Knowledge Discovery in Databases

n Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. Piatetsky-Shapiro, P. Smyth,

and R. Uthurusamy, 1996)

n 1995-1998 International Conferences on Knowledge Discovery in Databases

and Data Mining (KDD’95-98)

n Journal of Data Mining and Knowledge Discovery (1997)

n 1998 ACM SIGKDD, SIGKDD’1999-2001 conferences, and SIGKDD

Explorations

n More conferences on data mining

n PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM (2001), etc.

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Where to Find References?n Data mining and KDD

n Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc.n Journal: Data Mining and Knowledge Discovery, KDD Explorations

n Database systemsn Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT, ICDT, DASFAAn Journals: ACM-TODS, IEEE-TKDE, JIIS, J. ACM, VLDBJ, etc.

n AI & Machine Learningn Conferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory), etc.n Journals: Machine Learning, Artificial Intelligence, etc.

n Statisticsn Conferences: Joint Stat. Meeting, etc.n Journals: Annals of statistics, etc.

n Visualizationn Conference proceedings: CHI, ACM-SIGGraph, etc.n Journals: IEEE Trans. visualization and computer graphics, etc.

Web resources:1. DBLP2. Google3. Citeseer4. DL@lib

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Recommended Reference Booksn I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java

Implementations, Morgan Kaufmann, 2001

n C. C. Aggarwal, Data Mining: The Textbook, Springer, 2015��

n J. Leskovec, A. Rajaraman, and J. Ullman, Mining of Massive Datasets (v2.1), Cambridge University Press, 2014.

n Y. S. Abu-Mostafa, M. Magdon-Ismail, and H.-T. Lin, Learning From Data. AMLBook, 2012.

n J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2001

n D. J. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, MIT Press, 2001

n T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer-Verlag, 2001

n T. M. Mitchell, Machine Learning, McGraw Hill, 1997

n P-N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining,. Addison-Wesley, 2005

n S. M. Weiss and N. Indurkhya, Predictive Data Mining, Morgan Kaufmann, 1998

Jai’s Project (COMP9318, 2016s2)

n Problemn http://kentandlime.com.au/, a startup company helping

male customers to stay in fashion but out of the shops.n Status-quo:

n Ask questions, and stylists makes a list of recommended items, and send them to customers

n If happy, customers pay for the product. n Recommendation is the key!

n Challengesn Dirty datan Not an easy/typical recommendation system settingsn Customer feedbacksn Real-time recommendations

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http://www.news.com.au/lifestyle/fashion/fashion-trends/fashions-most-unlikely-trend-would-you-buy-clothes-chosen-for-you/news-story/8634b5f06f608b9f2fd7c27758f9c10a

Solutions - Highlight

n Use domain-knowledge and quick evaluations to guide the whole process

n Data preprocessingn Data source: CRM (profile) + NoSQL DB (transactions)

n Missing data: e.g., due to schema changes

n Data normalization: A’s XL = B’s L

n Data noise: k-means / binning

n Data selection: remove sparse columns/rows

n Feature engineeringn weight-to-height ratio

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Solutions – Highlight /2

n Product class clustering and predictionn Collaborative filtering with smoothing and

weightingn Content-based recommendation (solve the cold

start problem)n Incorporate customer feedbacksn Association rule mining:

n LSShirts_1, Shorts_2 è Socks_3n Emsemble of the above

n Plus many engineering efforts

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Results

n Test set: n Classification rate: 74%, on par with humans

n Deployed to production on 18-24 Nov 2016:n Customers rejecting on average 2.36 items out of a

basket of 10-12 items è (76.4%, 80.3%)n Latency: 2.3s

n Future work identifiedn e.g., seasonality

n Check Jai’s presentation slides for more details.

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